Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

Abstract

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption.

Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks

“…An aquila optimizer algorithm was introduced to improve the energy efficiency of the WSNs by selecting an optimal cluster head for transferring the packets from one node group to the other groups. The simulation result indicated an improvement in terms of energy availability and the number of nodes active in the connected space over the traditional LEACH and genetic algorithms [7]. A Q deep learning algorithm was developed to localize the position of the sensor nodes in three different symmetries.…”

A Comprehensive Survey on Aquila Optimizer

“…To increase the energy balance in clusters between sensor nodes during network communications and to decrease power consumption, Taha et al [ 26 ] offer a novel enhancement technique based on AO. The AO was utilized to identify the best cluster heads and make sure the network is clustering effectively and steadily, which reduces energy use and lengthens the lifespan of the network.…”

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

“…Rule-based learning predicts the output variable in decision tree method. The predictions about the output begin at the tree's root node [53]. Other characteristics are compared to the root node.…”